ADAPTATION AND LEARNING USING MULTIPLE MODELS, SWITCHING, AND TUNING

This article presents a general methodology for the design of adaptive control systems which can learn to operate efficiently in dynamical e nvironments possessing a high degree of uncertainty. Multiple models a re used to describe the different environments and the control is effe cted by switching to an appropriate controller followed by tuning or a daptation. The study of linear systems provides the theoretical founda tion for the approach and is described first. The manner in which such concepts can be extended to the control of non-linear systems using n eural networks is considered next. Towards the end of the article, the applications of the above methodology to practical robotic manipulato r control is described.